Traffic Control Domain Research Articles

Mechatronics Application and Performance Analysis in Intelligent Transportation Systems

This paper presents a comprehensive analysis of the integration of mechatronics in intelligent transportation systems (ITS), elucidating its functional capabilities and limitations. As urbanization accelerates and traffic demand rises, the ITS has emerged as vital solutions for addressing traffic congestion and safety issues. In this regard, mechatronics technology plays a key role by enhancing the intelligence of traffic management, thereby improving the overall efficiency and safety of transportation networks. Thus, the paper provides a detailed analysis of the mechatronics application in intelligent traffic signal control, road condition monitoring and management, and vehicle positioning and navigation. In the domain of intelligent traffic signal control, mechatronics technology facilitates adaptive signal adjustments through real-time data collection and analysis, effectively alleviating traffic congestion and enhancing road capacity. In the field of road condition monitoring and management, this technology combines advanced sensors and monitoring equipment to realize accurate identification and dynamic monitoring of vehicles, providing powerful support for traffic management and law enforcement. In addition, the use of mechatronics technology in critical areas such as vehicle positioning and navigation markedly enhances road utilization, effectively mitigates traffic congestion, as well as supports sustainable urban development initiatives. Meanwhile, the performance of mechatronics in ITS, including real-time, intelligence, safety and reliability, is analyzed, highlighting its remarkable effect in enhancing the overall performance of ITS.

Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques

ABSTRACT This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.

Traffic Light Control Systems: Requirements Engineering

Requirements Engineering (RE) is the most important activity and pivot phase in the software development life cycle. It consists mainly of requirements analysis, design, and specification. The application of RE in a business domain aims to generalize its different specific approaches by meta-models under which each specific approach is an instance or specialization. RE is being applied more and more in different business domains, and its application benefits are found to be valuable. Despite these active RE applications and their added values in different domains, the Traffic Light Control (TLC) domain has not yet been approached, regardless of its everyday interest. The work presented in this paper is part of a TLC enhancement project that explores RE's potential contributions to leveraging TLC quality. Thus, in this work, different recent specific approaches to TLC are analyzed, and a large part of this business domain's functional/ non-functional requirements are elicited and specified. Moreover, the traffic context-aware traffic light control systems (CTLC) are also considered. They are deeply analyzed and compared to the typical TLC systems. As a primary result, the tangling of different concerns was stated, and a separate concerns paradigm was applied. This leads to requirements specification through separated agents. This should lead to TLC and CTLC systems development and maintenance cost reduction. An important agent dealing with security aspects and their evolving technologies is introduced. The impact evaluations of the RE on some TLC and CTLC current approaches are also presented.

Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts

Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings.

Micro2vec: Anomaly detection in microservices systems by mining numeric representations of computer logs

This paper describes a study on log mining in the domain of microservices technologies. We focus on the detection of anomalies from logs, i.e., events requiring deeper inspection by analysts. Log mining is challenging in microservices systems due to the high number of heterogeneous logs. We present Micro2vec, a novel approach to mine numeric representations of computer logs without making assumptions on the format of underlying data and requiring no application knowledge; representations computed by Micro2vec are suited for anomaly detection. To cope with the lack of publicly-available datasets of labeled logs from production systems, we validate our approach by means of a mixture of direct measurements from logs, one-class classification experiments and generation of log variants. The study has been conducted in the context of a Clearwater IP Multimedia Subsystem setup consisting of microservices deployed in Docker containers, and on a real-world critical information system from the Air Traffic Control domain, which implements a communication model typically used with microservices.

A review of reinforcement learning applications in adaptive traffic signal control

In urban areas, the problem of recurring daily congestion is constantly increasing. A possible solution is seen in the application of adaptive traffic signal control (ATSC) systems for the control of signalized intersections. While available ATSC systems can achieve an increase in the Level of Service, the focus of ATSC research has shifted towards the application of reinforcement learning (RL) techniques, which allow the controller to learn the optimal control policy by direct interaction with the environment. This paper describes the fundamentals of traffic signal control, RL algorithms and approaches and their application to ATSC, with a discussion on the impact of connected and autonomous vehicles on future traffic signal control. In conclusion, a summary of open research questions and possible directions for future research in the domain of RL-based traffic signal control is given.

Novel Artificial Situational Awareness System is Comparable with Human Situational Awareness in the En-route Air Traffic Control Domain

AISA project introduces human-machine distributed situational awareness (SA) in en-route air traffic control. This paper is a result of the project's preliminary study. As a part of the project, a comparison between artificial and human situational awareness is performed. To investigate if the artificial SA could undistractedly monitor traffic, air traffic controller (ATCO) tasks are defined within Knowledge Graph (KG) system. KG system serves as a database with the ability to define relationships between data points. Human-in-the-loop simulations were performed to acquire needed data. ATCOs conducted different traffic scenarios which were later analysed and assessed regarding SA indicators defined in the paper. Tasks defined within the KG-based system can produce artificial SA that can successfully identify and complement all human SA degradation occurrences, contributing to Team SA. Therefore, defined tasks are sufficient to apply to the traffic data and to produce adequate machine SA.

An empirical analysis of error propagation in critical software systems

Error propagation analysis is a consolidated practice to gain insights into error modes and effects that pertain to the activation of faults in software systems. A variety of approaches, such as architecture-based, source code instrumentation and variable tracing, have been proposed so far to address software error propagation analysis. Although valuable, existing approaches entail a substantial degree of system internals’ knowledge, visibility and code manipulation that is not well-suited for real-life production environments. This paper proposes an empirical analysis of error propagation. We specifically address the challenges in using fault data and error events in the logs, which are a convenient byproduct of the system’s execution. The approach puts forth the construction of error reporting graphs. We apply the approach to 2,042 failure data points from two real-world critical systems from the Air Traffic Control domain by a top industry provider. The approach contributes to develop a deep understanding on error modes and propagation paths, which can be leveraged by practitioners to make informed decisions on the placement of error detection mechanisms.

Security Log Analysis in Critical Industrial Systems Exploiting Game Theoretic Feature Selection and Evidence Combination

Critical industrial systems have become profitable targets for cyber-attackers. Practitioners and administrators rely on a variety of data sources to develop security situation awareness at runtime. In spite of the advances in security information and event management products and services for handling heterogeneous data sources, analysis of proprietary logs generated by industrial systems keeps posing many challenges due to the lack of standard practices, formats, and threat models. This article addresses log analysis to detect anomalies, such as failures and misuse, in a critical industrial system. We conduct our study with a real-life system by a top leading industry provider in the air traffic control domain. The system emits massive volumes of highly-unstructured proprietary textual logs at runtime. We propose to extract quantitative metrics from logs and to detect anomalies by means of game theoretic feature selection and evidence combination. Experiments indicate that the proposed approach achieves high precision and recall at small tuning efforts.

Contextual filtering and prioritization of computer application logs for security situational awareness

Critical computer systems strongly rely on event logs to record the occurrence of normative and anomalous events occurring at runtime. In spite of the advances in Security Information and Event Management for handling monitoring data in production, event logs remain quite underutilized with respect to more conventional security data sources. Eliciting actionable knowledge for situational awareness poses many challenges in the case of logs emitted by industrial systems due to the lack of standard practices, formats and threat models.This paper addresses log analysis in a critical industrial system. We conduct our study with a real-life system by a top leading company in the Air Traffic Control domain, which emits massive volumes of unstructured proprietary logs. We propose a filtering method that pinpoints interesting events from logs, i.e., events that should be followed up by analysts. Experiments are done with logs from normative and misuse scenarios; moreover, we compare the outcome of our method with a reference filtering technique based on the conceptual clustering. Results indicate that the proposed method is effective to retain interesting events at remarkable precision and recall and to pinpoint misuse indicators. We overcome several drawbacks of existing filtering techniques, such as the need for labeled logs and domain knowledge, which makes our method easier to use by practitioners.

An integrated highly synchronous, high resolution, real time eye tracking system for dynamic flight movement

Electronic surveillance systems are being used rapidly today, ranging from a simple video camera to a complex biometric surveillance system for facial patterns and intelligent computer vision based surveillance systems, which are applied in many fields such as home monitoring, security surveillance of important places and mission critical tasks like air traffic control surveillance. Such systems normally involve a computer system and a human surveillance operator, who looks at the dynamic display to perform his surveillance tasks. Exploitation of shared information between these physical heterogeneous data capture systems with human operated functions is one emerging aspect in electronic surveillance that has yet to be addressed deeply. Hence, an innovative interaction interface for such knowledge extraction and representation is required. Such an interface should establish a data activity register frame which captures information depicting various surveillance activities at a specified spatial and time reference.This paper presents a real time eye tracking system, which integrates two sets of activity data in a highly dynamic changing and synchronous manner in real-time with respect to both spatial and time frames, through the “Dynamic Data Alignment and Timestamp Synchronisation Model”. This model matches the timestamps of the two data streams, aligns them to the same spatial reference frame before fusing them together into a data activity register frame. The Air Traffic Control (ATC) domain is used to illustrate this model, where experiments are conducted under simulated radar traffic situations with participants and their radar input data. Test results revealed that this model is able to synchronise the timestamp of the eye and dynamic display data, align both of these data spatially, while taking into account dynamic changes in space and time on a simulated radar display. This system can also distinguish and show variations in the monitoring behaviour of participants. As such, new knowledge can be extracted and represented through this innovative interface, which can then be applied to other applications in the field of electronic surveillance to unearth monitoring behaviour of the human surveillance operator.

Using agent-based simulations to address value tensions in design

This article proposes the use of agent-based simulation to analyze complex design trade-offs that involve value tensions. This is illustrated by a case study in the domain of train traffic control. In this case, agent-based simulation supported the involvement of stakeholders and increased understanding of a design trade-off.

Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain

Model-driven development (MDD) is an approach for supporting the development of software systems, in which high-level modeling artifacts drive the production of time and effort-consuming low-level artifacts, such as the source code. Previous studies of the MDD effectiveness showed that it significantly increases development productivity, because the development effort is focused on the business domain rather than technical issues. However, MDD was exploited in the context of agent-based development in a limited way, and most of the existing proposals demonstrated the effectiveness of using MDD in this context by argumentation or examples, lacking disciplined empirical analyses. In this paper, we explore the use of MDD for agent-based modeling and simulation in the adaptive traffic signal control (ATSC) domain, in which autonomous agents are in charge of managing traffic light indicators to optimize traffic flow. We propose an MDD approach, composed of a modeling language and model-to-code transformations for producing runnable simulations automatically. In order to analyze the productivity gains of our MDD approach, we compared the amount of design and implementation artifacts produced using our approach and traditional simulation platforms. Results indicate that our approach reduces the workload to develop agent-based simulations in the ATSC domain.

Evaluation of NextGen Air Traffic Control Alarms and Alerts Using Cognitive Walkthrough Method

In the air traffic control domain, there are no clear distinctions in the definition of critical safety messages, such as alarms and alerts. We therefore created a categorization scheme to distinguish alarms from the alerts and applied it to NextGen air traffic control systems: Standard Terminal Automation Replacement System (STARS), En Route Automation Modernization (ERAM), and Data Communications. Controllers then evaluated the effectiveness of the alarms and alerts of these NextGen systems. We used Cognitive Walkthrough method with the “thinking aloud” procedure to collect their spontaneous and immediate evaluation. Based on these evaluations, we present recommendations for the future NextGen air traffic control alarm and alert design.

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

Adapting a Human – Automation Trust Scale to an Air Traffic Management Environment

Model-based trajectory reconstruction with IMM smoothing and segmentation

This paper presents a new approach for off-line trajectory reconstruction in air traffic control domain. The proposed algorithm, called model-based reconstruction, performs an accurate IMM smoothing process whose parameters are modified along time according to the flight modes segmented from trajectory measurements. Its competitive performance is demonstrated through comparison with previous reconstruction methods used in ATC and with classical IMM smoothing, using simulated data.

Interactive image-based information visualization for aircraft trajectory analysis

Objectives: The objective of the presented work is to present novel methods for big data exploration in the Air Traffic Control (ATC) domain. Data is formed by sets of airplane trajectories, or trails, which in turn records the positions of an aircraft in a given airspace at several time instants, and additional information such as flight height, speed, fuel consumption, and metadata (e.g. flight ID). Analyzing and understanding this time-dependent data poses several non-trivial challenges to information visualization.Materials and methods: To address this Big Data challenge, we present a set of novel methods to analyze aircraft trajectories with interactive image-based information visualization techniques.As a result, we address the scalability challenges in terms of data manipulation and open questions by presenting a set of related visual analysis methods that focus on decision-support in the ATC domain. All methods use image-based techniques, in order to outline the advantages of such techniques in our application context, and illustrated by means of use-cases from the ATC domain.Results: For each considered use-case, we outline the type of questions posed by domain experts, data involved in addressing these questions, and describe the specific image-based techniques we used to address these questions. Further, for each of the proposed techniques, we describe the visual representation and interaction mechanisms that have been used to address the above-mentioned goals. We illustrate these use-cases with real-life datasets from the ATC domain, and show how our techniques can help end-users in the ATC domain discover new insights, and solve problems, involving the presented datasets.

Reliable and Timely Event Notification for Publish/Subscribe Services Over the Internet

The publish/subscribe paradigm is gaining attention for the development of several applications in wide area networks (WANs) due to its intrinsic time, space, and synchronization decoupling properties that meet the scalability and asynchrony requirements of those applications. However, while the communication in a WAN may be affected by the unpredictable behavior of the network, with messages that can be dropped or delayed, existing publish/subscribe solutions pay just a little attention to addressing these issues. On the contrary, applications such as business intelligence, critical infrastructures, and financial services require delivery guarantees with strict temporal deadlines. In this paper, we propose a framework that enforces both reliability and timeliness for publish/subscribe services over WAN. Specifically, we combine two different approaches: gossiping, to retrieve missing packets in case of incomplete information, and network coding, to reduce the number of retransmissions and, consequently, the latency. We provide an analytical model that describes the information recovery capabilities of our algorithm and a simulation-based study, taking into account a real workload from the Air Traffic Control domain, which evidences how the proposed solution is able to ensure reliable event notification over a WAN within a reasonable bounded time window.

Analysis of the performance characteristics of controllers’ strategies in en route air traffic control tasks

For addressing human factor issues in the air traffic control (ATC) domain, further comprehension of controllers' working methods during actual work is required. The objective of the present resear...

A New Class of Fuzzy Petri Nets for Knowledge Representation and Reasoning

This paper presents a new class of Petri nets called generalised fuzzy Petri nets. The new class extends the existing fuzzy Petri nets by introducing three operators in the form of triangular norms, which are supposed to function as substitute for the min, max and * algebraic product operators. To demonstrate the power and the usefulness of this model, an application of the generalised fuzzy Petri nets in the domain of train traffic control is provided. The new model is more flexible than the classical one as in the former class the user has the chance to define the input/output operators. The proposed approach can be used for knowledge representation and reasoning in decision support systems.